Simulating enhanced photo carrier collection in the multifinger photogate active pixel sensors
Bibliographic record
Abstract
Classic photo gate APS uses a MOS capacitor that can capture incident illumination with a potential well created under the photogate. The major drawback of such a technology is the absorption of shorter wavelength by the polysilicon gate resulting in a higher sensitivity in the red visible spectrum than in the blue range. To reduce this we previously had experimentally shown that a multifinger photo gate APS designs with 0.72Νm fingers implemented in the 0.18 μm CMOS technology have a significant increase in sensitivity of 1.7 times the standard photo gate APS. Using advanced 2-dimensional device simulations had shown that the fringing fields form the these fingers would create a potential well shape that approached that of the standard fully covered photo gate, but with large open areas which would have less optical absorption. Reducing the gate widths resulted in higher efficiency of photo carriers generated in the larger open areas while keeping the potential well shape desired. In this work, we use optical simulation package on the 2D device simulation tools to simulate the multi finger photo gate designs with white light illumination. Sensitivity of the pixel is calculated as the count of total number of photocarriers that are collected by the potential well for a given exposure cycle. All the multifinger designs achieved a significant increase in efficiency with respect to the standard photogate APS design, with the peak sensitivity of 550% by the 7finger design with a gate width of 0.25μm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".